class_docstr: Construct a CoreML KNearestNeighborsClassifier specification.
  
  Please see the Core ML Nearest Neighbors protobuf message for more information
  on KNearestNeighborsClassifier parameters.
  
  Examples
  --------
  .. sourcecode:: python
  
      from coremltools.models.nearest_neighbors import KNearestNeighborsClassifierBuilder
      from coremltools.models.utils import save_spec
  
      # Create a KNearestNeighborsClassifier model that takes 4-dimensional input data and outputs a string label.
      >>> builder = KNearestNeighborsClassifierBuilder(input_name='input',
      ...                                              output_name='output',
      ...                                              number_of_dimensions=4,
      ...                                              default_class_label='default_label')
  
      # save the spec by the builder
      >>> save_spec(builder.spec, 'knnclassifier.mlmodel')
      
docstr: Set the KNearestNeighborsClassifier to be updatable.
  
  Parameters
  ----------
  is_updatable
          Boolean
  
  Returns
  -------
  None
docstr_tok: ["Set","the","KNearestNeighborsClassifier","to","be","updatable",".","Parameters","--","--","--","--","--","is_updatable","Boolean","Returns","--","--","--","-","None"]
code: def is_updatable(self, is_updatable):
      """
      Set the KNearestNeighborsClassifier to be updatable.
  
      Parameters
      ----------
      is_updatable
              Boolean
  
      Returns
      -------
      None
      """
      self.spec.isUpdatable = is_updatable
  
imports: import numpy
  import coremltools
  from ...proto import FeatureTypes_pb2
  from ..None import datatypes
label_desc: Use image node_id 9 for calling the KNearestNeighborsClassifierBuilder obj's underlying member method code with example usage: obj.is_updatable(is_updatable) without return types